The limitations, drawbacks and/or disadvantages of technologies are as follows: Search Engines are based on ‘Boolean algebra’ eigenvector algorithms that are used to parse and filter information indices until the top page ranks are determined and displayed to the end user. Unfortunately, some specific keywords combinations may be too narrow and confound a search by hiding optimal results. Search Engines are predominately configured to perform one request to one reply search patterns. Each search is processed from the ground up without taking into account many requests belonging to one reply. A session consists of consecutive related and unrelated search requests to reach the final destination.
The Internet environment (U) can be construed as a complex and massive volume telecommunications network with billions of subscribers. The Search engine supercomputer analyzes information from the environment estimated to be billions of unique web pages, and then uses eigenvectors to determine the highest ranked pages from the end user's match criteria.
As explained, in the doctorial dissertation Designing Hot Billing Systems for Large Volume and/or Complex Networks© 1999, hereinafter referred as dissertation, “As the size of the environment increases the level of redundancy and tax burden of a system exponentially increases”. For this reason, it will be object of this invention to eliminate the tax burden created by the environment.
The Optimizer converts requests into ideas by eliminating Search Engines dependency on “Boolean algebra” using Caesar=s “divide and conquer” approach that limits reaching the top results. In contrast, the Optimizer using ‘deductive reasoning’ interprets keyword combinations as being part of an idea being formulated by both the left and the right sides of the brain. When a request is incomplete the Optimizer probabilistically supplies and inserts missing gaps of information. The dissertation teaches that a Vector CDR can be expressed as the summation of a plurality of valid vectors. The Optimizer matches/merges a plurality of partial vectors and then correlates them to create a resultant vector containing a collection of top (n) web pages possessing informational certitude.
In a nutshell, the ‘Boolean algebra’ mimics Watson like criminal investigation methods for finding the best results. Whereas Optimizer uses Triangulation Deductive Reasoning to convert the end user's typed keywords into a meaningful idea, insert missing gaps of information, perform the steps of: 1) Association, 2) Relevancy and 3) Likelihood to create an optimal environment express in hundreds of web pages and finally 4) ‘Cherry Pick’, by physically read the content of each web page and then perform probabilistically vector weight comparisons to identify the best response.
Description of the ‘HIVE’ Supercomputer
Dissertation and Intelligent Component Billing System
The ‘HIVE’ is a massive parallel distributed managerial hierarchical structured supercomputer (hereinafter referred as “HIVE”) that performs the following:
1) Transform Data:
The ‘HIVE’ cleans, standardizes and organizes the spaghetti of the environment by gathering, analyzing, distilling, managing, organizing and distributing the huge amount of information with a ‘HIVE’ that removes redundancy, latency and the organizational tax burden.
2) Synchronize Tasks:
The “HIVE” is also a decentralized parallel clustered large-scale supercomputer consisting of a plurality of nodes, which are specifically arranged in three tiers. The summit tier coordinates and executes global tasks. The middle tier coordinates and executes regional tasks. The lower tier coordinates and executes localized tasks and processes the lion share of non-critical transactions. The summit node of each tier synchronizes tasks by sending command messages that assigns the fuzzy logic state of each node belonging to its chain of command.
3) Lateral and Vertical Synergy:
A tier consisting of groups of nodes that are independent from other groups of nodes. Each tier partition performs mission critical tasks within their domain and works in parallel with other partitions of the same tier. Each node can shunt available resources using lateral and vertical synergy with parent, sibling or subordinate nodes to maximize available resources. Each node continuously analyzes its own environment current conditions and forward chains summary information until reaching the summit. At this point, the summit nodes rearward chain command messages with instructions that regulate priorities, resources availability, and notify each subordinate with coordinated and synchronized tasks constraints taking into account present network conditions to avoid saturation, clog and eliminate the ‘spaghetti of the environment’.
4) Removes the “Spaghetti of the Environment”:
Applying steps 1 to 3 the “spaghetti of the environment” is eliminated and then the “HIVE” creates command messages that are also known as environment bitmap data. Command messages coordinate and synchronize each node to operate at maximal output capacity. Each node operates without adversely affecting the network flow of data. The “HIVE” maximizes available throughput and limits the exponential rate of growth of complexity as the size of the environment increases.
5) Vector CDR:
Can be expressed as the summation of a plurality of valid vectors. A telecommunication call begins for its origin or Leg A and travels through a call trajectory circuit by circuit until reaching the destination or Leg B. The ‘HIVE’ assigns for Leg A and for Leg B one node, except when both origin and destination belong to the same circuit and thus the same node. When Leg A and Leg B do not belong to the same circuit, Tandem circuits are then used to bind them, which are expressed as Legs (X, Y, Z).
6) Forward Chaining:
Using SS7 conventions messages that are sent through the managerial hierarchy that originate from subordinates and are sent to their hierarchical superiors are referred as forward chained messages. A request message that originates outside of the domain or environment of the ‘HIVE’ is also considered a forward chaining message.
7) Rearward Chaining:
Using SS7 conventions messages that are sent through the managerial hierarchy that originate from hierarchical superiors and are sent to their subordinates are referred as rearward chained messages. A response message that is sent outside of the domain or environment of the ‘HIVE’ derived from a previously received request is also considered a rearward-chaining message.
8) Environmental Bitmap Data:
Also known the summary reports made by each node by the artificial intelligence. Each subordinate of the ‘HIVE’ during predefined time intervals creates a summary analysis of environment network conditions from its own perspective and also how much throughput is available as buffer. These messages are forwarded chained tier by tier in the managerial hierarchy and each hierarchically superior matches/merges the collective summary analysis of each of its subordinates to know the conditions of the environment of its chain of command, and this process is done until the summit tier has all the necessary information of the exact conditions of the environment, so it can optimally decide, control, manage and instructs and inform its subordinates. The summit tier then rearward chains the (global) environmental bitmap information to its subordinates. Middle Tier nodes control independent environments or regional domains, e.g. fixed, wireless and IP networks, or SE USA, and can also create and update their subordinates by reward chaining (regional) environmental bitmap messages.
9) Artificial Intelligence:
The “HIVE” consists of a plurality of nodes, where each one is programmed with Artificial Intelligence programs to perform predefined ad hoc tasks that are logistical rationalized based on the current conditions of the environment. The ‘HIVE’ is synonymous with the Superset (U). The cluster is divided into three geospatial tiers: a) Global, b) Regional, and c) Local. Each tier has the following functions:                a) Provisioning        b) Total Quality Management or (TQM)        c) Data Manipulation        d) Management Information Systems (or MIS)        e) Expert Information Systems (or EIS)        f) Inventory Control        
All nodes work collectively and independently from each other, and still simultaneously and in parallel perform the tasks of analyzing, evaluating, gathering and processing information from the environment in real time. From incipiency upon receiving the fuzzy logic piece of information that triggers a new task or update pending activities.
Each node is assigned to a Superset (I), Set (I, J), or Subset (I, J, K) cluster tier, and is assigned to geospatial domains (X) or global, (Y) or regional, and (Z) local to create sub clusters Elements (I, J, K, X, Y, Z) that help to build the managerial hierarchy as follows:
The Summit Tier coordinates the database used for Business Intelligence and Invoicing via the Internet that allows users to have access to their information in real time. The Middleware Tier manages regional geographical area. The Lower Tier controls a plurality of points of presence and collectively constitutes the workhorse of the system.
Each node synchronizes the latest inventory every predefined cycle, and then the artificial intelligence programming will optimize its organizational management logistics.
Nodes can request to members of the same group any excess buffer resources to complete a task using vertical and lateral synergy. Parent nodes can use their chain of command to coordinate the resources of their subordinates to complete a task. Members of different regional cluster can synergistically share and collaborate to process tasks.
Each node is able to replace and perform the organizational task of at least one node, so that collectively the “HIVE”; engulfs a global supplier.
The “HIVE” has specialized interaction means with the environment to gather, distill, analyze and then standardize and convert the raw information into primed lingua franca data, which in turn is quantified, qualified, organized and transformed, so that Information Entropy is achieved and thus removes the chaos and anarchy or “Spaghetti Phenomena”.
Each lingua franca message is primed by the ‘HIVE’ as single version of the truth vector trajectory containing all pertinent transactional segments information. The vector trajectory assigns a hierarchical owner and activates all nodes related to the transaction so that nodes can communicate amongst themselves via forward and rearward chaining.
Proactively the human resources of the organization can use business intelligence software to send parameters to the ‘HIVE’. Enabling individuals to directly control their own network, and then send command instructions with the latest conditions of the environment so the ‘HIVE’ can optimally analyze, coordinate, prioritize and synchronize throughput.
Middleware and Summit nodes perform data warehouse functions, and are programmed to monitor and control their chains of command. They act as virtual simulation of the organization. Lower nodes are designed to remove redundancy, geographically distribute activities, and then correlate and update information.
The ‘HIVE’ monitors the limited resources and capacities of the network to avoid taxing available throughput in real time. Each node can create, plot and update purchase orders as soon as new relevant messages from the environment are detected.
Upon receiving environment command instructions each node can manage and organize the flow of information of their subordinates from predefined point A to point B routes to avoid clogs and saturation. Each node via synergy attempts to maximize throughput, and assign, prioritize and shares with other nodes that have substantial buffer resources, since unused resources are considered waste, which is one the confounding variable that is directly related in creating the “Spaghetti Phenomena”.
Network traffic is segregated and analyzed by tier as the informational traffic is measured based on the latest command instructions and known routing throughput limitations of each given domain. The summit nodes of each tier performs the non obvious task of removing complexity in order to be a real time system by eliminating data redundancy, filtering, quantifying, qualifying data as good or garbage, and minimizing waste before beginning to transmit the data through the managerial hierarchy system.
Nodes are programmed to remove the “Spaghetti Phenomena” at the point of attack, that is perform one transaction at a time, so that the ‘HIVE’ can reach Information Entropy at the organizational level to be considered a real time invention.
Summit and Middleware nodes stabilize the flow of financial conditions, inventories, shipping costs and tariffs required for billing, and update the XLDB database with trending statistics that in turn are used to optimize resources and available bandwidth.
Each node is programmed to be autonomous, and through means of the managerial hierarchical synergy, can work in parallel with others nodes to work as a single unit. Each node processes network information and then simulate, plot, map, tract and vector each message to create a virtual instance of the organizational environment.
After the ‘HIVE’ eliminates the “Spaghetti Phenomena”, Informational Entropy is achieved and thus a state of balance, harmony and proportion exists. The ‘HIVE’ distributed configuration removes the need for a central mainframe. Consequently, a real time solution consists of synergistically synchronizing all the “HIVE” functions.
Each node has its own location identification means and must be assigned to one geospatial specific domain cluster such as local, regional or global. Every single activity and purchase order is processed in parallel, starting from the point of origin and ending at the point of destination. The “HIVE” then rearward chains the routing vector information through the simulation network to the point of origin.
The “HIVE” analyzes, evaluates and synchronizes the best usage of network resources as follows:                a) Administers, coordinates, controls, manages, and transforms the network.        b) Uses Business Intelligence to predict when a customer becomes dissatisfied.        c) Manages the flow of money in real time.        d) Sends summarized information packets to their organizational subordinates.        e) Assigns costs to each activity and limiting each resource.        f) Uses synergy to load balances the demand on the organization=s resources.        g) Works always at maximal assigned throughput.        h) Redundant with throughput reserves to compensate for network faults.        i) Works in parallel with the simulated Legacy System.        j) Parent nodes create command messages with resource allocation instructions.        k) Creates partial vectors that measure one independent environment.        l) Match/merge all partial vectors to create the final billing entity or purchase order.        